Project description
Machine learning for objective and fast bipolar disorder diagnosis
Thanks to advancements in psychiatry and psychology, patients with previously misunderstood or misdiagnosed disorders now receive more accurate diagnoses and improved treatment. However, bipolar disorder (BD), which affects a patient’s quality of life and impacts 3 % of the population, requires fast, objective, and accurate diagnosis (a challenging task for psychiatrists within a short timeframe). Supported by the Marie Skłodowska-Curie Actions programme, the AI-DIAGNOSE project aims to develop a rapid, automated tool using machine learning to detect BD and psychotic symptoms by using audiovisual biomarkers in patients. The tool will identify eye and speech patterns associated with BD or psychosis symptoms, enhancing diagnostic accuracy and efficiency.
Objective
Bipolar disorder (BD) is a chronic and debilitating mental disorder, that affects 2-3% of the population. It impacts quality of life, cognition, and is a leading cause of suicide and all-cause mortalities. Most patients are taken into clinical care during acute episodes, which puts the burden on psychiatrists to make fast, yet accurate diagnostic decisions. However, unlike most medical conditions, psychiatric diagnoses are subjective. This paired with the complexity of its clinical presentation, BD is the most misdiagnosed and underdiagnosed psychiatric condition. More objective scales used in research lack clinical application, due to time constraints and high burden on the patient. AI-DIAGNOSE wants to disrupt the state of the art of BD diagnosis through a completely novel approach: developing an automatized and fast tool for objective detection of BD and psychotic symptoms based on physiological audiovisual biomarkers and machine learning (ML). The timing of the project is supported through recent evidence, from the host, the applicant, and others, showing that speech and eye movement are promising physiological biomarkers. In a pilot study, I found that ML algorithms based on speech patterns could predict the presence of psychiatric diagnosis, and differentiate patients with and without psychosis. Eyetracking datasets provide insights regarding information processing patterns, and have shown potential as diagnostic biomarkers. Although eye movement and speech patterns are promising biomarkers as they can be acquired fast and without putting high burden on the patient, they have not been combined yet for psychiatric diagnostic purposes. The project will be the first to develop such a multi-modal ML diagnostic tool for BD and psychosis in BD. We will test its accuracy against the research gold standard in the field within a large patient cohort (140 patients, 70 controls). If successful, this will a major step towards precision medicine within BD and psychiatry
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2022-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08007 BARCELONA
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.